Introduction

An Exploratory Analysis of Gender Differences in the Baron-Cohen’s Empathizing Quotient and Systemizing Quotient Tests

Column

Introduction to the Study

Data Overview

For my final presentation, I decided to look at a collection of responses to an online version of clinical psychologist Simon Baron-Cohen’s Empathizing (EQ) and Systemizing (SQ) Quotient tests. These tests are designed to measure if there are sex differences in whether a person with autism thinks of the world around them in terms of systems and classifications or through empathizing. The data set contains the answers to 120 different questions using a Likert scale, with 60 regarding the empathizing quotient and 60 questions regarding the systemizing quotient.

My project will attempt to answer the questions below:

  • Is this a reliable psychological scale?
  • Which gender tends to score higher in empathizing?
  • Which gender tends to score higher in systemizing?
  • Are there any common themes in responses from different age groups? Different genders?

Glimpse of Data

Rows: 8,514
Columns: 125
$ E1       <int> 4, 3, 3, 2, 1, 4, 1, 2, 2, 4, 3, 4, 3, 2, 3, 4, 3, 4, 2, 2, 3…
$ E2       <int> 3, 3, 1, 1, 1, 1, 3, 2, 2, 1, 3, 1, 2, 1, 3, 1, 1, 2, 1, 2, 1…
$ E3       <int> 3, 3, 2, 4, 3, 3, 3, 3, 2, 3, 2, 2, 4, 3, 2, 2, 2, 2, 3, 3, 3…
$ E4       <int> 2, 1, 4, 2, 1, 2, 2, 2, 1, 4, 1, 3, 2, 3, 3, 3, 2, 2, 1, 2, 4…
$ E5       <int> 2, 1, 4, 3, 3, 4, 1, 2, 2, 3, 1, 4, 3, 2, 2, 2, 1, 4, 1, 3, 3…
$ E6       <int> 2, 2, 2, 2, 4, 1, 2, 3, 3, 3, 4, 4, 1, 2, 2, 1, 3, 1, 4, 3, 2…
$ E7       <int> 2, 2, 3, 4, 1, 4, 1, 2, 3, 1, 1, 2, 2, 2, 2, 2, 2, 4, 1, 2, 3…
$ E8       <int> 3, 2, 3, 4, 2, 1, 2, 3, 3, 4, 4, 3, 3, 4, 2, 1, 2, 1, 2, 3, 3…
$ E9       <int> 4, 3, 2, 2, 3, 1, 3, 3, 1, 1, 2, 1, 3, 3, 1, 2, 3, 1, 1, 2, 2…
$ E10      <int> 4, 1, 3, 3, 3, 4, 3, 2, 3, 1, 4, 1, 3, 1, 1, 3, 1, 1, 3, 4, 4…
$ E11      <int> 3, 4, 1, 2, 3, 3, 4, 3, 3, 2, 2, 1, 2, 1, 3, 3, 1, 2, 3, 2, 1…
$ E12      <int> 4, 2, 1, 4, 1, 1, 3, 2, 3, 1, 1, 1, 3, 2, 2, 1, 2, 1, 2, 2, 1…
$ E13      <int> 2, 2, 4, 3, 3, 4, 1, 2, 2, 4, 3, 3, 4, 3, 4, 3, 1, 4, 4, 2, 4…
$ E14      <int> 1, 2, 1, 2, 1, 1, 1, 2, 2, 4, 1, 3, 3, 2, 2, 2, 2, 4, 1, 2, 2…
$ E15      <int> 1, 3, 4, 4, 1, 4, 1, 2, 1, 4, 4, 4, 4, 2, 1, 2, 3, 2, 1, 1, 4…
$ E16      <int> 3, 3, 3, 4, 4, 4, 3, 2, 4, 2, 4, 4, 2, 3, 3, 2, 2, 1, 4, 3, 3…
$ E17      <int> 2, 3, 1, 2, 2, 4, 2, 3, 4, 4, 2, 3, 2, 4, 2, 2, 3, 2, 2, 2, 2…
$ E18      <int> 4, 2, 1, 1, 2, 1, 2, 2, 1, 4, 2, 1, 2, 3, 1, 1, 2, 1, 3, 2, 1…
$ E19      <int> 3, 1, 3, 3, 2, 4, 1, 2, 3, 4, 1, 1, 4, 2, 2, 4, 3, 2, 1, 3, 3…
$ E20      <int> 1, 1, 4, 3, 3, 4, 1, 2, 3, 4, 2, 4, 3, 4, 3, 3, 4, 4, 1, 2, 4…
$ E21      <int> 3, 1, 2, 1, 3, 1, 2, 2, 3, 1, 3, 4, 2, 3, 3, 2, 1, 1, 1, 2, 4…
$ E22      <int> 4, 3, 4, 4, 4, 4, 3, 3, 2, 3, 4, 3, 2, 3, 3, 3, 3, 3, 4, 3, 3…
$ E23      <int> 3, 4, 1, 2, 3, 1, 4, 3, 4, 4, 4, 2, 1, 3, 3, 2, 3, 1, 3, 2, 1…
$ E24      <int> 4, 2, 2, 1, 1, 1, 3, 2, 1, 1, 4, 1, 1, 1, 1, 1, 3, 1, 1, 2, 1…
$ E25      <int> 1, 1, 3, 2, 2, 4, 1, 2, 3, 2, 2, 4, 4, 3, 2, 2, 2, 2, 2, 2, 4…
$ E26      <int> 4, 4, 1, 2, 3, 1, 3, 3, 2, 1, 4, 3, 1, 4, 2, 1, 2, 1, 1, 3, 1…
$ E27      <int> 4, 2, 3, 2, 3, 4, 1, 2, 2, 1, 2, 2, 3, 4, 3, 1, 1, 4, 4, 3, 2…
$ E28      <int> 3, 4, 1, 4, 4, 3, 4, 3, 4, 2, 3, 1, 1, 4, 4, 1, 4, 4, 4, 3, 1…
$ E29      <int> 3, 1, 3, 3, 1, 2, 1, 2, 1, 4, 1, 4, 4, 2, 1, 2, 1, 1, 1, 2, 2…
$ E30      <int> 1, 3, 4, 3, 2, 4, 4, 3, 3, 4, 4, 4, 2, 2, 3, 3, 2, 3, 3, 1, 4…
$ E31      <int> 4, 2, 2, 3, 3, 1, 2, 3, 3, 2, 4, 1, 2, 3, 2, 2, 3, 1, 2, 2, 2…
$ E32      <int> 2, 2, 1, 2, 3, 1, 1, 2, 1, 1, 4, 2, 2, 2, 2, 1, 4, 1, 4, 3, 1…
$ E33      <int> 1, 1, 4, 3, 2, 4, 1, 2, 3, 4, 2, 4, 3, 2, 3, 3, 1, 4, 1, 2, 2…
$ E34      <int> 1, 1, 4, 3, 3, 4, 2, 2, 2, 2, 3, 4, 3, 3, 2, 4, 3, 3, 1, 2, 3…
$ E35      <int> 2, 2, 2, 2, 3, 2, 3, 3, 1, 2, 2, 1, 2, 2, 2, 2, 2, 3, 2, 2, 1…
$ E36      <int> 4, 2, 3, 2, 2, 3, 3, 2, 2, 4, 4, 2, 2, 2, 3, 2, 3, 4, 3, 2, 3…
$ E37      <int> 1, 1, 3, 2, 3, 4, 1, 2, 2, 4, 4, 2, 3, 2, 2, 3, 1, 4, 2, 1, 3…
$ E38      <int> 2, 4, 3, 2, 3, 3, 4, 3, 3, 4, 4, 1, 1, 2, 2, 2, 4, 3, 4, 2, 3…
$ E39      <int> 2, 2, 4, 2, 4, 2, 4, 2, 2, 1, 4, 4, 2, 4, 3, 3, 1, 4, 4, 3, 3…
$ E40      <int> 4, 4, 2, 2, 1, 2, 2, 3, 3, 2, 3, 1, 3, 1, 2, 2, 3, 3, 2, 2, 2…
$ E41      <int> 2, 1, 3, 3, 3, 3, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 3, 3, 1, 3…
$ E42      <int> 4, 3, 2, 2, 3, 3, 3, 3, 4, 4, 3, 1, 4, 4, 3, 1, 1, 2, 4, 3, 2…
$ E43      <int> 2, 4, 1, 2, 2, 3, 3, 3, 1, 1, 3, 1, 3, 3, 1, 2, 3, 1, 3, 2, 2…
$ E44      <int> 3, 4, 4, 2, 4, 4, 4, 3, 4, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 2, 4…
$ E45      <int> 4, 4, 3, 4, 4, 1, 4, 3, 4, 3, 4, 1, 1, 4, 3, 3, 4, 2, 4, 2, 3…
$ E46      <int> 3, 3, 4, 3, 4, 4, 4, 3, 2, 4, 4, 3, 4, 4, 4, 4, 4, 3, 3, 3, 3…
$ E47      <int> 3, 4, 1, 3, 2, 3, 3, 3, 2, 3, 2, 3, 2, 2, 1, 4, 2, 1, 2, 2, 3…
$ E48      <int> 2, 1, 3, 2, 2, 4, 2, 2, 1, 2, 3, 1, 4, 1, 1, 3, 1, 2, 1, 1, 2…
$ E49      <int> 1, 1, 3, 1, 2, 4, 1, 2, 3, 4, 1, 4, 1, 2, 1, 3, 2, 4, 1, 3, 2…
$ E50      <int> 3, 3, 4, 4, 2, 4, 2, 3, 2, 4, 2, 3, 3, 3, 3, 4, 2, 1, 3, 2, 3…
$ E51      <int> 3, 4, 2, 1, 3, 1, 4, 3, 4, 1, 3, 3, 3, 2, 3, 2, 2, 1, 4, 3, 2…
$ E52      <int> 3, 3, 1, 3, 4, 1, 2, 2, 3, 2, 4, 3, 2, 2, 3, 1, 3, 1, 2, 2, 2…
$ E53      <int> 4, 1, 4, 4, 2, 4, 2, 2, 3, 2, 1, 1, 2, 2, 3, 4, 1, 3, 1, 2, 4…
$ E54      <int> 2, 3, 1, 3, 4, 1, 3, 2, 3, 4, 4, 3, 2, 2, 1, 1, 1, 3, 4, 1, 2…
$ E55      <int> 3, 1, 3, 3, 3, 4, 2, 2, 3, 3, 2, 2, 3, 2, 3, 3, 2, 4, 2, 2, 3…
$ E56      <int> 4, 3, 2, 2, 3, 4, 3, 3, 3, 1, 3, 1, 2, 3, 1, 3, 3, 1, 2, 2, 2…
$ E57      <int> 4, 4, 1, 2, 1, 1, 4, 3, 3, 1, 3, 1, 1, 3, 2, 2, 2, 1, 3, 3, 2…
$ E58      <int> 3, 3, 3, 4, 4, 4, 3, 3, 4, 3, 4, 4, 4, 4, 3, 4, 4, 3, 3, 3, 3…
$ E59      <int> 4, 2, 4, 3, 3, 4, 3, 3, 3, 4, 4, 3, 4, 1, 3, 3, 3, 3, 3, 3, 4…
$ E60      <int> 2, 4, 1, 3, 3, 1, 3, 2, 2, 3, 4, 4, 2, 3, 2, 1, 4, 3, 4, 3, 1…
$ S1       <int> 4, 3, 3, 2, 1, 4, 1, 2, 2, 4, 3, 4, 3, 2, 3, 4, 3, 4, 2, 2, 3…
$ S2       <int> 3, 3, 1, 1, 1, 1, 3, 2, 2, 1, 3, 1, 2, 1, 3, 1, 1, 2, 1, 2, 1…
$ S3       <int> 3, 3, 2, 4, 3, 3, 3, 3, 2, 3, 2, 2, 4, 3, 2, 2, 2, 2, 3, 3, 3…
$ S4       <int> 2, 1, 4, 2, 1, 2, 2, 2, 1, 4, 1, 3, 2, 3, 3, 3, 2, 2, 1, 2, 4…
$ S5       <int> 2, 1, 4, 3, 3, 4, 1, 2, 2, 3, 1, 4, 3, 2, 2, 2, 1, 4, 1, 3, 3…
$ S6       <int> 2, 2, 2, 2, 4, 1, 2, 3, 3, 3, 4, 4, 1, 2, 2, 1, 3, 1, 4, 3, 2…
$ S7       <int> 2, 2, 3, 4, 1, 4, 1, 2, 3, 1, 1, 2, 2, 2, 2, 2, 2, 4, 1, 2, 3…
$ S8       <int> 3, 2, 3, 4, 2, 1, 2, 3, 3, 4, 4, 3, 3, 4, 2, 1, 2, 1, 2, 3, 3…
$ S9       <int> 4, 3, 2, 2, 3, 1, 3, 3, 1, 1, 2, 1, 3, 3, 1, 2, 3, 1, 1, 2, 2…
$ S10      <int> 4, 1, 3, 3, 3, 4, 3, 2, 3, 1, 4, 1, 3, 1, 1, 3, 1, 1, 3, 4, 4…
$ S11      <int> 3, 4, 1, 2, 3, 3, 4, 3, 3, 2, 2, 1, 2, 1, 3, 3, 1, 2, 3, 2, 1…
$ S12      <int> 4, 2, 1, 4, 1, 1, 3, 2, 3, 1, 1, 1, 3, 2, 2, 1, 2, 1, 2, 2, 1…
$ S13      <int> 2, 2, 4, 3, 3, 4, 1, 2, 2, 4, 3, 3, 4, 3, 4, 3, 1, 4, 4, 2, 4…
$ S14      <int> 1, 2, 1, 2, 1, 1, 1, 2, 2, 4, 1, 3, 3, 2, 2, 2, 2, 4, 1, 2, 2…
$ S15      <int> 1, 3, 4, 4, 1, 4, 1, 2, 1, 4, 4, 4, 4, 2, 1, 2, 3, 2, 1, 1, 4…
$ S16      <int> 3, 3, 3, 4, 4, 4, 3, 2, 4, 2, 4, 4, 2, 3, 3, 2, 2, 1, 4, 3, 3…
$ S17      <int> 2, 3, 1, 2, 2, 4, 2, 3, 4, 4, 2, 3, 2, 4, 2, 2, 3, 2, 2, 2, 2…
$ S18      <int> 4, 2, 1, 1, 2, 1, 2, 2, 1, 4, 2, 1, 2, 3, 1, 1, 2, 1, 3, 2, 1…
$ S19      <int> 3, 1, 3, 3, 2, 4, 1, 2, 3, 4, 1, 1, 4, 2, 2, 4, 3, 2, 1, 3, 3…
$ S20      <int> 1, 1, 4, 3, 3, 4, 1, 2, 3, 4, 2, 4, 3, 4, 3, 3, 4, 4, 1, 2, 4…
$ S21      <int> 3, 1, 2, 1, 3, 1, 2, 2, 3, 1, 3, 4, 2, 3, 3, 2, 1, 1, 1, 2, 4…
$ S22      <int> 4, 3, 4, 4, 4, 4, 3, 3, 2, 3, 4, 3, 2, 3, 3, 3, 3, 3, 4, 3, 3…
$ S23      <int> 3, 4, 1, 2, 3, 1, 4, 3, 4, 4, 4, 2, 1, 3, 3, 2, 3, 1, 3, 2, 1…
$ S24      <int> 4, 2, 2, 1, 1, 1, 3, 2, 1, 1, 4, 1, 1, 1, 1, 1, 3, 1, 1, 2, 1…
$ S25      <int> 1, 1, 3, 2, 2, 4, 1, 2, 3, 2, 2, 4, 4, 3, 2, 2, 2, 2, 2, 2, 4…
$ S26      <int> 4, 4, 1, 2, 3, 1, 3, 3, 2, 1, 4, 3, 1, 4, 2, 1, 2, 1, 1, 3, 1…
$ S27      <int> 4, 2, 3, 2, 3, 4, 1, 2, 2, 1, 2, 2, 3, 4, 3, 1, 1, 4, 4, 3, 2…
$ S28      <int> 3, 4, 1, 4, 4, 3, 4, 3, 4, 2, 3, 1, 1, 4, 4, 1, 4, 4, 4, 3, 1…
$ S29      <int> 3, 1, 3, 3, 1, 2, 1, 2, 1, 4, 1, 4, 4, 2, 1, 2, 1, 1, 1, 2, 2…
$ S30      <int> 1, 3, 4, 3, 2, 4, 4, 3, 3, 4, 4, 4, 2, 2, 3, 3, 2, 3, 3, 1, 4…
$ S31      <int> 4, 2, 2, 3, 3, 1, 2, 3, 3, 2, 4, 1, 2, 3, 2, 2, 3, 1, 2, 2, 2…
$ S32      <int> 2, 2, 1, 2, 3, 1, 1, 2, 1, 1, 4, 2, 2, 2, 2, 1, 4, 1, 4, 3, 1…
$ S33      <int> 1, 1, 4, 3, 2, 4, 1, 2, 3, 4, 2, 4, 3, 2, 3, 3, 1, 4, 1, 2, 2…
$ S34      <int> 1, 1, 4, 3, 3, 4, 2, 2, 2, 2, 3, 4, 3, 3, 2, 4, 3, 3, 1, 2, 3…
$ S35      <int> 2, 2, 2, 2, 3, 2, 3, 3, 1, 2, 2, 1, 2, 2, 2, 2, 2, 3, 2, 2, 1…
$ S36      <int> 4, 2, 3, 2, 2, 3, 3, 2, 2, 4, 4, 2, 2, 2, 3, 2, 3, 4, 3, 2, 3…
$ S37      <int> 1, 1, 3, 2, 3, 4, 1, 2, 2, 4, 4, 2, 3, 2, 2, 3, 1, 4, 2, 1, 3…
$ S38      <int> 2, 4, 3, 2, 3, 3, 4, 3, 3, 4, 4, 1, 1, 2, 2, 2, 4, 3, 4, 2, 3…
$ S39      <int> 2, 2, 4, 2, 4, 2, 4, 2, 2, 1, 4, 4, 2, 4, 3, 3, 1, 4, 4, 3, 3…
$ S40      <int> 4, 4, 2, 2, 1, 2, 2, 3, 3, 2, 3, 1, 3, 1, 2, 2, 3, 3, 2, 2, 2…
$ S41      <int> 2, 1, 3, 3, 3, 3, 2, 2, 2, 4, 2, 2, 2, 2, 2, 2, 2, 3, 3, 1, 3…
$ S42      <int> 4, 3, 2, 2, 3, 3, 3, 3, 4, 4, 3, 1, 4, 4, 3, 1, 1, 2, 4, 3, 2…
$ S43      <int> 2, 4, 1, 2, 2, 3, 3, 3, 1, 1, 3, 1, 3, 3, 1, 2, 3, 1, 3, 2, 2…
$ S44      <int> 3, 4, 4, 2, 4, 4, 4, 3, 4, 4, 4, 4, 2, 3, 3, 3, 3, 4, 4, 2, 4…
$ S45      <int> 4, 4, 3, 4, 4, 1, 4, 3, 4, 3, 4, 1, 1, 4, 3, 3, 4, 2, 4, 2, 3…
$ S46      <int> 3, 3, 4, 3, 4, 4, 4, 3, 2, 4, 4, 3, 4, 4, 4, 4, 4, 3, 3, 3, 3…
$ S47      <int> 3, 4, 1, 3, 2, 3, 3, 3, 2, 3, 2, 3, 2, 2, 1, 4, 2, 1, 2, 2, 3…
$ S48      <int> 2, 1, 3, 2, 2, 4, 2, 2, 1, 2, 3, 1, 4, 1, 1, 3, 1, 2, 1, 1, 2…
$ S49      <int> 1, 1, 3, 1, 2, 4, 1, 2, 3, 4, 1, 4, 1, 2, 1, 3, 2, 4, 1, 3, 2…
$ S50      <int> 3, 3, 4, 4, 2, 4, 2, 3, 2, 4, 2, 3, 3, 3, 3, 4, 2, 1, 3, 2, 3…
$ S51      <int> 3, 4, 2, 1, 3, 1, 4, 3, 4, 1, 3, 3, 3, 2, 3, 2, 2, 1, 4, 3, 2…
$ S52      <int> 3, 3, 1, 3, 4, 1, 2, 2, 3, 2, 4, 3, 2, 2, 3, 1, 3, 1, 2, 2, 2…
$ S53      <int> 4, 1, 4, 4, 2, 4, 2, 2, 3, 2, 1, 1, 2, 2, 3, 4, 1, 3, 1, 2, 4…
$ S54      <int> 2, 3, 1, 3, 4, 1, 3, 2, 3, 4, 4, 3, 2, 2, 1, 1, 1, 3, 4, 1, 2…
$ S55      <int> 3, 1, 3, 3, 3, 4, 2, 2, 3, 3, 2, 2, 3, 2, 3, 3, 2, 4, 2, 2, 3…
$ S56      <int> 4, 3, 2, 2, 3, 4, 3, 3, 3, 1, 3, 1, 2, 3, 1, 3, 3, 1, 2, 2, 2…
$ S57      <int> 4, 4, 1, 2, 1, 1, 4, 3, 3, 1, 3, 1, 1, 3, 2, 2, 2, 1, 3, 3, 2…
$ S58      <int> 3, 3, 3, 4, 4, 4, 3, 3, 4, 3, 4, 4, 4, 4, 3, 4, 4, 3, 3, 3, 3…
$ S59      <int> 4, 2, 4, 3, 3, 4, 3, 3, 3, 4, 4, 3, 4, 1, 3, 3, 3, 3, 3, 3, 4…
$ S60      <int> 2, 4, 1, 3, 3, 1, 3, 2, 2, 3, 4, 4, 2, 3, 2, 1, 4, 3, 4, 3, 1…
$ EQ       <int> 54, 57, 26, 18, 29, 35, 23, 34, 38, 35, 37, 27, 32, 36, 65, 4…
$ SQ       <int> 14, 12, 57, 33, 19, 61, 10, 7, 23, 49, 17, 52, 42, 19, 28, 42…
$ accuracy <int> 98, 95, 97, 65, 90, 80, 75, 90, 95, 99, 90, 90, 97, 80, 85, 7…
$ gender   <fct> Female, Female, Male, Female, Male, Female, Female, Female, F…
$ age      <int> 21, 32, 17, 17, 21, 20, 25, 23, 32, 20, 19, 32, 20, 20, 25, 2…

Column

Variable Explanation

  • Variables E1 - E60: Empathizing quotient questions
  • Variables S1 - S60: Systemizing quotient questions
  • Age: Age of participant
  • Gender: Gender of participant
  • Accuracy: How accurate the participant thought their answer was

Total EQ/SQ

Column

Empathizing Quotient

Systemizing Quotient

Column

Analysis

These boxplots break down the average score of participants for the empathizing and systemizing quotients by gender.

As illustrated by the figures, females tend to score higher on the empathizing quotient and males tend to score higher on the systemizing quotient. There is a significant amount of outliers for males on both empathizing and systemizing, and for females on the systemizing quotient.

Female EQ

Column

Age 17-24

Age 24-34

Column

Analysis

The figures to the left illustrates the distributions of total EQ scores from females in two distinct age groups. Women aged 17-24 have a slight right, positive skew in their distribution of total EQ scores, while women aged 24-34 have a slightly more symmetrical distribution in their total EQ scores. It is worth noting that the number of participants signficiantly decreases as age increases.

Male EQ

Column

Age 17-24

Age 24-34

Column

Analysis

The figures to the left illustrates the distributions of total EQ scores from males in two distinct age groups. Men aged 17-24 have a small right skew in their distribution of total EQ scores, while men aged 24-34 have a slightly smaller right skew in their distribution of total EQ scores.

Female SQ

Column

Age 17-24

Age 24-34

Column

Analysis

The figures to the left illustrate the distributions of total SQ scores from females in two distinct age groups. Both figures show a strong right skew in their distributions, and when compared to the distributions of EQ scores, which suggests that women regardless of age group tend to score lower on the systemizing quotient than the empathizing quotient.

Male SQ

Column

Age 17-24

Age 24-34

Column

Analysis

The figures to the left illustrates the distributions of total EQ scores from males in two distinct age groups. Men aged 17-24 have a small right skew in their distribution of total SQ scores, while men aged 24-34 have a slightly smaller left skew in their distribution of total SQ scores. When compared to the distributions of EQ scores, this suggests that men tend to score higher on the Systemizing Quotient than the Empathizing Quotient regardless of age group. It is worth noting that for both males and females, the number of participants decreases as the age of the participants increases across all figures.

Reliability

Column

Female E12

Male E12

Female S16

Male S16

Column

Analysis

The four pie charts to the left breaks down the distribution of answers between men and women to either question E12 or S16. These two questions are focused on the dynamics of friendships and both share a similar message, which is one of the many reasons why the validity of the scale is questioned. The distribution of answers for both males and females to question E12 indicated a majority of participants disagree with the statement, yet the distribution of answers to question S16 indicate a majority of participants agree with the statement.

Conclusion

Column

Conclusions

  • Which gender tends to score higher in empathizing? Systemizing?

Women tend to score higher in empathizing, and men tend to score higher in systemizing in every applicable test, which corroborates societal gender norms and expectations.

  • Are there any common themes in responses from different age groups? Different genders?

The total scores on both the empathizing quotient and the systemizing quotient tend to vary more as age increases for both males and females. This suggests that thinking becomes less rigid and more fluid as we age and mature.

  • Is this a reliable psychological scale?

No, this is not a reliable psychological scale. There are a significant number of outliers in the boxplots breaking down total EQ and SQ scores for males and females, and as age increases total scores tend to vary and become less extreme. On top of what has been discovered in this presentation, the Empathizing-Systemizing theory has been heavily criticized and debated within the psychological science community. Many critiques show a very weak correlation between empathizing and systemizing, stating that they are more likely distinct dimensions of each individual personality. Developmental psychologists have critiqued this theory and responded by stating that women scoring higher in empathy and men scoring higher in systemizing may be a result of societal gender norms and expectations that are taught, and are not biological in nature.

Limitations

This data set was retrieved from the open source psychometrics project on an online database. These results were collected from participants who took this test online, unsupervised, without proper verification of a diagnosis of autism, and with no attention check questions, meaning the reliability and validity of the scores is questionable. There are multiple ways data can be manipulated with in psychological analyses, such as p-hacking (increasing the chances of finding statistically significant results that support the theory/hypothesis) that I was unable to verify did not happen. I filtered the data to attempt to control for this as much as possible.

Column

Future Directions

It would be interesting to see if the results from this study are consistent with a control study done where a researcher is able to watch the participant and has a control group of participants who do not have autism as well. An even closer analysis looking at distributions of answers to individual questions on both scales, breaking down age groups to be even smaller, and conducting statistical analyses such as finding p-values and Independent T-Tests would be beneficial in supporting or falsifying the theory.

About

Column

Bio

My name is Emily Antognoli and I am a senior psychology major at the University of Dayton. I have an expected graduation date of May 2025.

After I spent my sophomore spring semester studying abroad in Ireland, I changed my major to psychology and became a member of both Dr. Losee’s social psychology research lab and Dr. Erin Kunz’s social psychology research lab. I spent this semester working in a psychology-focused internship at The Brook Center here on campus. My research interests are broad and interdisciplinary, and my goal is to obtain a PhD in social psychology and become a professor and researcher at a university.

Column

Me

A photo of me on vacation in Honduras, with a curious capuchin monkey on my head

A photo of me on vacation in Honduras, with a curious capuchin monkey on my head 

---
title: "EQSQ Scores"
author: Emily Antognoli
output: 
  flexdashboard::flex_dashboard:
    theme:
      bootswatch: yeti
    orientation: columns
    vertical_layout: fill
    source_code: embed
---

```{r setup, include=FALSE}
library(flexdashboard)
library(tidyverse)
library(DT)
library(plotly)
library(knitr)
final_project_data <- read.csv("~/RSTUDIO Labs/MTH 209/MTH209/LABS/finaldata.csv")
```

```{r}
final_project_data$gender <- factor(final_project_data$gender, 
                                    levels = c(1, 2, 3, 0), 
                                    labels = c("Male", "Female", "Other", "Not Included"))
filtered_data <- final_project_data %>%
  filter(gender != "Not Included" & gender != "Other" & age >= 17 & age <= 34)

filtered_data$gender <- droplevels(filtered_data$gender)
```

Introduction
===

An Exploratory Analysis of Gender Differences in the Baron-Cohen’s Empathizing Quotient and Systemizing Quotient Tests

Column {.tabset data-width=650}
---
### Introduction to the Study

**Data Overview**

For my final presentation, I decided to look at a collection of responses to an [online version](https://www.kaggle.com/datasets/yamqwe/empathizingsystemizing-test) of clinical psychologist Simon Baron-Cohen's Empathizing (EQ) and Systemizing (SQ) Quotient tests. These tests are designed to measure if there are sex differences in whether a person with autism thinks of the world around them in terms of systems and classifications or through empathizing. The data set contains the answers to 120 different questions using a Likert scale, with 60 regarding the empathizing quotient and 60 questions regarding the systemizing quotient.

My project will attempt to answer the questions below:

  - Is this a reliable psychological scale?
  - Which gender tends to score higher in empathizing?
  - Which gender tends to score higher in systemizing?
  - Are there any common themes in responses from different age groups? Different genders?

### Glimpse of Data

```{r}
glimpse(filtered_data)
```

Column {.tabset data-width=350}
---

### Variable Explanation
  - **Variables E1 - E60**: Empathizing quotient questions
  - **Variables S1 - S60**: Systemizing quotient questions
  - **Age**: Age of participant
  - **Gender**: Gender of participant
  - **Accuracy**: How accurate the participant thought their answer was

Total EQ/SQ
===

Column {.tabset data-width=550}
---

### Empathizing Quotient
```{r boxplot1, fig.align='center', out.width="85%", echo=FALSE}
boxplot(filtered_data$EQ ~ filtered_data$gender, main = "Distribution of Empathizing Quotient Scores by Gender",
        xlab = "Participant Gender", ylab = "Total Empathizing Quotient Score", cex.lab = 1.25, cex.axis = 1.25, col = "#54d2d2")
```

### Systemizing Quotient 
```{r boxplot2, fig.align='center', out.width="85%", echo=FALSE}
boxplot(filtered_data$SQ ~ filtered_data$gender, main = "Distribution of Systemizing Quotient Scores by Gender",
        xlab = "Participant Gender", ylab = "Total Systemizing Quotient Score", cex.lab = 1.25, cex.axis = 1.25, col = "#FA8072")
```

Column {.tabset data-width=550}
---

### Analysis

These boxplots break down the average score of participants for the empathizing and systemizing quotients by gender.

As illustrated by the figures, females tend to score higher on the empathizing quotient and males tend to score higher on the systemizing quotient. There is a significant amount of outliers for males on both empathizing and systemizing, and for females on the systemizing quotient. 

Female EQ
===

Column {.tabset data-width=650}
---

### Age 17-24

```{r barchart1, fig.align='center', out.width="85%", echo=FALSE}
filtered_females_17_24 <- filtered_data %>%
  filter(gender == "Female" & age >= 17 & age <= 24)

ggplot(filtered_females_17_24,
       aes(x = EQ)) +
  geom_bar(fill = "#7b3294") + 
  labs(title = "Total EQ Score for Females", subtitle = "Aged 17-24", x = "EQ Score", y = "Number of Participants") +
  theme(text = element_text(size = 20))
```


### Age 24-34

```{r barchart2, fig.align='center', out.width="85%", echo=FALSE}
filtered_females_24_34 <- filtered_data %>%
  filter(gender == "Female" & age >= 24 & age <= 34)

ggplot(filtered_females_24_34,
       aes(x = EQ)) +
  geom_bar(fill = "#7b3294") + 
  labs(title = "Total EQ Score for Females", subtitle = "Aged 24-34", x = "EQ Score", y = "Number of Participants") +
  theme(text = element_text(size = 20))
```

Column {.tabset data-width=350}
---
### Analysis

The figures to the left illustrates the distributions of total EQ scores from females in two distinct age groups. Women aged 17-24 have a slight right, positive skew in their distribution of total EQ scores, while women aged 24-34 have a slightly more symmetrical distribution in their total EQ scores. It is worth noting that the number of participants signficiantly decreases as age increases. 

Male EQ
===

Column {.tabset data-width=650}
---

### Age 17-24

```{r barchart3, fig.align='center', out.width="85%", echo=FALSE}
filtered_males_17_24 <- filtered_data %>%
  filter(gender == "Male" & age >= 17 & age <= 24)

ggplot(filtered_males_17_24,
       aes(x = EQ)) +
  geom_bar(fill = "#018571") + 
  labs(title = "Total EQ Score for Males", subtitle = "Aged 17-24", x = "EQ Score", y = "Number of Participants") +
  theme(text = element_text(size = 20))
```

### Age 24-34

```{r barchart4, fig.align='center', out.width="85%", echo=FALSE}
filtered_males_24_34 <- filtered_data %>%
  filter(gender == "Male" & age >= 24 & age <= 34)

ggplot(filtered_males_24_34,
       aes(x = EQ)) +
  geom_bar(fill = "#018571") + 
  labs(title = "Total EQ Score for Males", subtitle = "Aged 24-34", x = "EQ Score", y = "Number of Participants") +
  theme(text = element_text(size = 20))
```

Column {.tabset data-width=350}
---

### Analysis 

The figures to the left illustrates the distributions of total EQ scores from males in two distinct age groups. Men aged 17-24 have a small right skew in their distribution of total EQ scores, while men aged 24-34 have a slightly smaller right skew in their distribution of total EQ scores.  

Female SQ
===

Column {.tabset data-width=650}
---

### Age 17-24

```{r barchart5, fig.align='center', out.width="85%", echo=FALSE}
ggplot(filtered_females_17_24,
       aes(x = SQ)) +
  geom_bar(fill = "#7b3294") + 
  labs(title = "Total SQ Score for Females", subtitle = "Aged 17-24", x = "SQ Score", y = "Number of Participants") +
  theme(text = element_text(size = 20))
```


### Age 24-34

```{r barchart6, fig.align='center', out.width="85%", echo=FALSE}
ggplot(filtered_females_24_34,
       aes(x = SQ)) +
  geom_bar(fill = "#7b3294") + 
  labs(title = "Total SQ Score for Females", subtitle = "Aged 24-34", x = "SQ Score", y = "Number of Participants") +
  theme(text = element_text(size = 20))
```

Column {.tabset data-width=350}
---

### Analysis 

The figures to the left illustrate the distributions of total SQ scores from females in two distinct age groups. Both figures show a strong right skew in their distributions, and when compared to the distributions of EQ scores, which suggests that women regardless of age group tend to score lower on the systemizing quotient than the empathizing quotient. 

Male SQ
===

Column {.tabset data-width=650}
---

### Age 17-24

```{r barchart7, fig.align='center', out.width="85%", echo=FALSE}
ggplot(filtered_males_17_24,
       aes(x = SQ)) +
  geom_bar(fill = "#018571") + 
  labs(title = "Total SQ Score for Males", subtitle = "Aged 17-24", x = "SQ Score", y = "Number of Participants") +
  theme(text = element_text(size = 20))
```

### Age 24-34

```{r barchart8, fig.align='center', out.width="85%", echo=FALSE}
ggplot(filtered_males_24_34,
       aes(x = SQ)) +
  geom_bar(fill = "#018571") + 
  labs(title = "Total SQ Score for Males", subtitle = "Aged 24-34", x = "SQ Score", y = "Number of Participants") +
  theme(text = element_text(size = 20))
```

Column {.tabset data-width=350}
---

### Analysis 

The figures to the left illustrates the distributions of total EQ scores from males in two distinct age groups. Men aged 17-24 have a small right skew in their distribution of total SQ scores, while men aged 24-34 have a slightly smaller left skew in their distribution of total SQ scores. When compared to the distributions of EQ scores, this suggests that men tend to score higher on the Systemizing Quotient than the Empathizing Quotient regardless of age group. It is worth noting that for both males and females, the number of participants decreases as the age of the participants increases across all figures. 

Reliability
===

Column {.tabset data-width=650}
---

### Female E12
```{r pie1, fig.align='center', out.width="85%", echo=FALSE}
bad_scale3 <- filtered_data %>%
  filter(E12 != "0" & gender == "Female")

H <- table(bad_scale3$E12)
percent <- round(100*H/sum(H),1)
pie_labels <- paste(percent, "%", sep="")
pie(H, main = "Distribution of Answers by Females to Question E12", sub = "Friendships/Relationships are too difficult, so I tend to not bother with them", labels = pie_labels, col = c("#1f78b4", "#b2df8a", "#a6cee3", "#33a02c"))
legend("topright", c("1 - Strongly Disagree", "2 - Slightly Disagree", "3 - Slightly Agree", "4 - Strongly Agree"), cex = 0.5, fill = c("#1f78b4", "#b2df8a", "#a6cee3", "#33a02c"))

```

### Male E12
```{r}
bad_scale2 <- filtered_data %>%
  filter(E12 != "0" & gender == "Male")

H <- table(bad_scale2$E12)
percent <- round(100*H/sum(H),1)
pie_labels <- paste(percent, "%", sep="")
pie(H, main = "Distribution of Answers by Males to Question E12", sub = "Friendships/Relationships are too difficult, so I tend to not bother with them", labels = pie_labels, col = c("#1f78b4", "#b2df8a", "#a6cee3", "#33a02c"))
legend("topright", c("1 - Strongly Disagree", "2 - Slightly Disagree", "3 - Slightly Agree", "4 - Strongly Agree"), cex = 0.5, fill = c("#1f78b4", "#b2df8a", "#a6cee3", "#33a02c"))

```

### Female S16
```{r}
bad_scale4 <- filtered_data %>%
  filter(S16 != "0" & gender == "Female")

H <- table(bad_scale4$S16)
percent <- round(100*H/sum(H),1)
pie_labels <- paste(percent, "%", sep="")
pie(H, main = "Distribution of Answers by Females to Question S16", sub = "I am bad about keeping in tough with old friends", labels = pie_labels, col = c("#ca0020", "#92c5de", "#f4a582", "#0571b0"))
legend("topright", c("1 - Strongly Disagree", "2 - Slightly Disagree", "3 - Slightly Agree", "4 - Strongly Agree"), cex = 0.5, fill = c("#ca0020", "#92c5de", "#f4a582", "#0571b0"))

```

### Male S16
```{r}
bad_scale1 <- filtered_data %>%
  filter(S16 != "0" & gender == "Male")

H <- table(bad_scale1$S16)
percent <- round(100*H/sum(H),1)
pie_labels <- paste(percent, "%", sep="")
pie(H, main = "Distribution of Answers by Males to Question S16", sub = "I am bad about keeping in tough with old friends", labels = pie_labels, col = c("#ca0020", "#92c5de", "#f4a582", "#0571b0"))
legend("topright", c("1 - Strongly Disagree", "2 - Slightly Disagree", "3 - Slightly Agree", "4 - Strongly Agree"), cex = 0.5, fill = c("#ca0020", "#92c5de", "#f4a582", "#0571b0"))

```

Column {.tabset data-width=650}
---

### Analysis

The four pie charts to the left breaks down the distribution of answers between men and women to either question E12 or S16. These two questions are focused on the dynamics of friendships and both share a similar message, which is one of the many reasons why the validity of the scale is questioned. The distribution of answers for both males and females to question E12 indicated a majority of participants disagree with the statement, yet the distribution of answers to question S16 indicate a majority of participants agree with the statement. 

Conclusion
===

Column {.tabset data-width=500}
---

### Conclusions

  - Which gender tends to score higher in empathizing? Systemizing?

Women tend to score higher in empathizing, and men tend to score higher in systemizing in every applicable test, which corroborates societal gender norms and expectations. 

  - Are there any common themes in responses from different age groups? Different genders?
  
The total scores on both the empathizing quotient and the systemizing quotient tend to vary more as age increases for both males and females. This suggests that thinking becomes less rigid and more fluid as we age and mature. 

  - Is this a reliable psychological scale?
  
No, this is not a reliable psychological scale. There are a significant number of outliers in the boxplots breaking down total EQ and SQ scores for males and females, and as age increases total scores tend to vary and become less extreme. On top of what has been discovered in this presentation, the Empathizing-Systemizing theory has been heavily criticized and debated within the psychological science community. Many critiques show a very weak correlation between empathizing and systemizing, stating that they are more likely distinct dimensions of each individual personality. Developmental psychologists have critiqued this theory and responded by stating that women scoring higher in empathy and men scoring higher in systemizing may be a result of societal gender norms and expectations that are taught, and are not biological in nature. 

### Limitations 

This data set was retrieved from the open source psychometrics project on an online database. These results were collected from participants who took this test online, unsupervised, without proper verification of a diagnosis of autism, and with no attention check questions, meaning the reliability and validity of the scores is questionable. There are multiple ways data can be manipulated with in psychological analyses, such as p-hacking (increasing the chances of finding statistically significant results that support the theory/hypothesis) that I was unable to verify did not happen. I filtered the data to attempt to control for this as much as possible. 

Column {.tabset data-width=500}
---

**Future Directions**

It would be interesting to see if the results from this study are consistent with a control study done where a researcher is able to watch the participant and has a control group of participants who do not have autism as well. An even closer analysis looking at distributions of answers to individual questions on both scales, breaking down age groups to be even smaller, and conducting statistical analyses such as finding p-values and Independent T-Tests would be beneficial in supporting or falsifying the theory. 


About 
===

Column {.tabset data-width=500}
---

### Bio

My name is Emily Antognoli and I am a senior psychology major at the University of Dayton. I have an expected graduation date of May 2025. 


After I spent my sophomore spring semester studying abroad in Ireland, I changed my major to psychology and became a member of both Dr. Losee's social psychology research lab and Dr. Erin Kunz's social psychology research lab. I spent this semester working in a psychology-focused internship at The Brook Center here on campus. My research interests are broad and interdisciplinary, and my goal is to obtain a PhD in social psychology and become a professor and researcher at a university. 

Column {.tabset data-width=500}
---

### Me

```{r Picture, fig.width= 3, echo = FALSE, fig.cap= "A photo of me on vacation in Honduras, with a curious capuchin monkey on my head"}

knitr::include_graphics("Project Photo.jpg")
```